There is a need to intensify research efforts on improving productivity of indigenous vegetables in South Africa. One research avenue is operationalizing remote sensing techniques to monitor crop health status. This study aimed at characterising the spectral properties of Chinese cabbage (<i>Brassica Rapa L. subspecies Chinensis</i>) grown under varying fertilizer treatments: nitrogen (0 kg/ha, 75 kg/ha, 125 kg/ha, 175 kg/ha and 225 kg/ha), phosphorus (0 kg/ha, 9.4 kg/ha, 15.6, 21.9 kg/ha and 28.1 kg/ha) and potassium (0 kg/ha, 9.4  kg/ha, 15.6 kg/ha, 21.9 kg/ha and 28.1 kg/ha). Visible and infrared spectral measurements were taken from a total of 60 samples inside the laboratory. Contiguous spectral regions were plotted to show spectral profiles of the different fertilizer treatments and then classified using gradient boosting and random forest classifiers. ANOVA revealed the potential of spectral reflectance data in discriminating different fertiliser treatments from crops. There was also a significant difference between the capabilities of the two classifiers. Gradient boost model (GBM) yielded higher classification accuracies than random forest (RF). The important variables identified by each model improved the classification accuracy. Overall, the results indicate a potential for the use of spectroscopy in monitoring food quality parameters, thereby reducing the cost of traditional methods. Further research into advanced statistical analysis techniques is needed to improve the accuracy with which fertiliser concentrations in crops could be quantified. The random forest model particularly requires improvements.